In the medical context, imaging of patients plays an important role in numerous scenarios. In one type of nuclear medical imaging known as Single Photon Emission Computed Tomography (SPECT), the primary imaging task is to accurately determine and depict the spatial 3D or 4D distribution of a radioactive isotope (radioisotope) used as a tracer (radiotracer) in the imaged object. A tomographic gamma camera is used to acquire multiple projections from sufficiently many different viewing angles in data space (e.g., 2D space), and a computer performs tomographic image reconstruction to generate an image in a higher-dimensional (e.g., 3D or 4D) image space. For example, a gamma photon-emitting radioisotope may be introduced into a patient's body, and any of various techniques can be used to bind the radioisotope to a location of interest in the body. Typically, one or more gamma cameras are attached to a gantry of the imaging system, and the gantry rotates and/or shifts, causing the gamma camera(s) to rotate and/or shift relative to the patient who is positioned on a bed. Detectors of the gamma camera(s) acquire projection data at each orientation by detecting gamma photons emitted by the radioisotope, resulting in a projection data set. Using the techniques of tomographic reconstruction, an image is generated based on the projection data set.
Because the duration of the acquisition of a projection view (dwell time) for each projection in a SPECT acquisition is typically at least as long as the period of a typical human respiratory cycle, it is impossible to require a patient to hold his/her breath during the acquisition of all projections needed for SPECT imaging to avoid any artifacts from respiratory motion. Typically, the patient continues to breathe during the acquisition, and because the dwell time or exposure is at least as long as the respiratory cycle, blurring of the projection view occurs. This blurring is a known problem in, e.g. cardiac SPECT imaging, where because of the volume change of the lungs the diaphragm impinges and moves the heart itself. Other examples are motion of tumors in lungs and the liver.
To address the problem of blurring caused by cycle motion, one uses the well known technique of gating, whereby one subdivides an acquisition into multiple (e.g. G=8) time bins (also known as time gates) during each of which there is relatively little motion of body parts. Each projection view now results in G gated projections corresponding to the individual gates, and proper booking now generates a total of G projection data sets that can be reconstructed into G reconstructed volumes, or images each depicting a different motion state. One can then evaluate the images separately. In order to perform the gating process, a physiological trigger is used. In general, if the cyclical motion is measured one can extract, e.g., trigger and amplitude. A common approach for obtaining such a signal has been to use an external sensor such as a pressure sensor, spirometer, piezoelectric belt, or camera to obtain information regarding respiratory state. There are several problems associated with the use of an external sensor for tracking respiratory state. In most cases the measurement is not a direct measurement of the internal organ, but rather the consequence of the internal motion, such as pressure change or visual chest movement. In addition, such a sensor represents extra hardware that must be installed, calibrated, and synchronized with the imaging system. A sensor affixed to a patient may be uncomfortable, particularly if it is bulky. Additionally, the presence of one or more detectors near the patient imposes space constraints and may render large external sensors impractical.
Some work has previously been pursued on approaches for generating a surrogate respiratory signal that are data driven, i.e., based on the projection data itself, without the need for an external sensor. But, known data-driven approaches do not work well with traditional SPECT. For example, some prior data-driven approaches have been based on tracking count rate variations and image centroids in the PET context. But, traditional SPECT is not tomographically consistent over time, as acquisitions involve sequential planar projections, and only at the end of the last view can a tomograph can be formed. PET, on the other hand, uses a ring detector, allowing tomographic data to be acquired simultaneously from all view angles at each time point. In the latter case, motion is always seen as blur, while in traditional SPECT, different blurring may have been measured at different viewing angles, resulting not only in a final image that is blurred, but one that may contain more severe artifacts, such as shape distortions or “bumpiness.” Regardless of whether PET or SPECT image formation is used, emission tomography data is always noisy because of insufficient counting statistics resulting from the desire to limit the dose burden to the patient, and reasonably short acquisition times to allow for an efficient clinical workflow. In such imaging scenarios lacking temporal tomographic consistency and involving noisy data, any data driven method must be robust with respect to noise and flexible enough to accommodate different motion patterns encountered from different view angles.